1. Technical Field Text
The disclosed embodiments are related to recommendation systems and more particularly to systems and methods for mitigating the item cold-start problem in collaborative filtering recommendation systems by characterizing new items using the interactions and characterizations of users that were selected to explore these new items in an online environment.
2. Background Information
Recommendation systems aim to present users with the most relevant items (e.g., movies, songs, advertisements, etc.) by predicting the user interest. Typically, they base their predictions on predefined features and user activity. User activity refers to numerical ratings or binary interactions users provide that reflect their interest in certain items. Techniques that rely on features are known as content-based while techniques that rely solely on user activity are known as collaborative filtering (CF). CF is widely used in recommenders due to its high accuracy, good scalability, and ability to execute without content analysis for feature extraction.
CF suffers when dealing with new items or users, known as the cold-start problem. This problem arises because the system does not have relevant interactions for the new entity (user or item). Thus, when a new entity (user or item) appears and there are no relevant historical interactions, the CF recommender cannot model the new entity reliably. While users and items are usually represented similarly in the latent vector space, these two problems are essentially different. The user cold-start problem, in which a new user joins the system, is commonly addressed by interviewing the new user and asking her to rate several key items. Unfortunately, the item cold-start problem is not as simple because items cannot be interviewed and typically there are no users willing to rate every new item. Another difference is that in most settings the number of users is much larger than the number of items, hence a typical item usually gets more ratings than an individual user provides, which may affect the modeling.
To model new items, CF recommenders select users for exploration and record their interactions with the new items. However, recommender systems typically have only a handful of slots to present items, for both exploration and recommendation, and since their goal is to present recommendations, the exploration process must be efficient and accurate. Not only are CF recommenders expected to meet these two goals but they also face the challenge that users arrive in an online fashion, namely systems do not know which users will arrive and when. This enforces the exploration process to decide whether to present to users new items immediately upon their arrival.
A common approach to mitigate the item cold-start problem is by providing additional attributes of the new items to the recommender systems. This approach is known as the hybrid approach since it combines CF with content-based methods. Past approaches have used a hybrid approach based on Boltzmann machines. In other approaches, a regression-based latent factor model in which the items' and users' latent vectors are obtained from low-rank matrix decomposition of a matrix whose products are weight matrices and attribute matrices. In still other approaches, the regression-based latent factor model is improved by solving a convex optimization problem to estimate the weight matrices.
Other works have addressed a different setting in which there are few ratings to the new items but there is no item content or context information. They showed that new items' latent factor vectors could be estimated by a linear combination of the raters' latent factor vectors and their ratings (without retraining the model). A common approach to obtain these ratings for new items to bootstrap their modeling devotes a portion of the user traffic for random exploration of the new items.
Thus, there exists a technical problem of how to implement a collaborative filtering recommendation system when dealing with relatively new items. It would be beneficial if such a system were efficient, requiring few impressions for new items, and accurate, obtaining good results with the few impressions. The particular context of the problem is described herein as an advertising system and recommendation of new items. However, the solutions described herein may be readily extended to other systems using collaborative filtering that experience the cold start problem.